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# ScholarCopilot-Data-v1 |
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ScholarCopilot-Data-v1 contains the corpus data and embedded vectors of [Scholar Copilot](https://github.com/TIGER-AI-Lab/ScholarCopilot). Scholar Copilot improves the academic writing process by seamlessly integrating automatic text completion and intelligent citation suggestions into a cohesive, human-in-the-loop AI-driven pipeline. Designed to enhance productivity and creativity, it provides researchers with high-quality text generation and precise citation recommendations powered by iterative and context-aware Retrieval-Augmented Generation (RAG). |
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The current version of Scholar Copilot leverages a state-of-the-art 7-billion-parameter language model (LLM) trained on the complete Arxiv full paper corpus. This unified model for retrieval and generation is adept at making context-sensitive decisions about when to cite, what to cite, and how to generate coherent content based on reference papers. |
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## 🌟 Key Features |
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- ** 📝 Next-3-Sentence Suggestions: Facilitates writing by predicting the next sentences with automatic retrieval and citation of relevant reference papers. |
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- ** 📚 Citation Suggestions on Demand: Provides precise, contextually appropriate paper citations whenever needed. |
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- ** ✨ Full Section Auto-Completion: Assists in brainstorming and drafting comprehensive paper content and structure. |
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The current version of ScholarCopilot primarily focuses on the introduction and related work sections of academic papers. We will support full-paper writing in future releases. |
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